text: import argparse
  import os
  import os.path as osp
  import warnings
  
  import mmcv
  import torch
  from mmcv import Config, DictAction
  from mmcv.cnn import fuse_conv_bn
  from mmcv.fileio.io import file_handlers
  from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
  from mmcv.runner import get_dist_info, init_dist, load_checkpoint
  from mmcv.runner.fp16_utils import wrap_fp16_model
  
  from mmaction.apis import multi_gpu_test, single_gpu_test
  from mmaction.datasets import build_dataloader, build_dataset
  from mmaction.models import build_model
  from mmaction.utils import register_module_hooks
  
  
  def parse_args():
      parser = argparse.ArgumentParser(
          description='MMAction2 test (and eval) a model')
      parser.add_argument('config', help='test config file path')
      parser.add_argument('checkpoint', help='checkpoint file')
      parser.add_argument(
          '--out',
          default=None,
          help='output result file in pkl/yaml/json format')
      parser.add_argument(
          '--fuse-conv-bn',
          action='store_true',
          help='Whether to fuse conv and bn, this will slightly increase'
          'the inference speed')
      parser.add_argument(
          '--eval',
          type=str,
          nargs='+',
          help='evaluation metrics, which depends on the dataset, e.g.,'
          ' "top_k_accuracy", "mean_class_accuracy" for video dataset')
      parser.add_argument(
          '--gpu-collect',
          action='store_true',
          help='whether to use gpu to collect results')
      parser.add_argument(
          '--tmpdir',
          help='tmp directory used for collecting results from multiple '
          'workers, available when gpu-collect is not specified')
      parser.add_argument(
          '--options',
          nargs='+',
          action=DictAction,
          default={},
          help='custom options for evaluation, the key-value pair in xxx=yyy '
          'format will be kwargs for dataset.evaluate() function (deprecate), '
          'change to --eval-options instead.')
      parser.add_argument(
          '--eval-options',
          nargs='+',
          action=DictAction,
          default={},
          help='custom options for evaluation, the key-value pair in xxx=yyy '
          'format will be kwargs for dataset.evaluate() function')
      parser.add_argument(
          '--cfg-options',
          nargs='+',
          action=DictAction,
          default={},
          help='override some settings in the used config, the key-value pair '
          'in xxx=yyy format will be merged into config file. For example, '
          "'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'")
      parser.add_argument(
          '--average-clips',
          choices=['score', 'prob', None],
          default=None,
          help='average type when averaging test clips')
      parser.add_argument(
          '--launcher',
          choices=['none', 'pytorch', 'slurm', 'mpi'],
          default='none',
          help='job launcher')
      parser.add_argument('--local_rank', type=int, default=0)
      args = parser.parse_args()
      if 'LOCAL_RANK' not in os.environ:
          os.environ['LOCAL_RANK'] = str(args.local_rank)
  
      if args.options and args.eval_options:
          raise ValueError(
              '--options and --eval-options cannot be both '
              'specified, --options is deprecated in favor of --eval-options')
      if args.options:
          warnings.warn('--options is deprecated in favor of --eval-options')
          args.eval_options = args.options
      return args
  
  
  def main():
      args = parse_args()
  
      cfg = Config.fromfile(args.config)
  
      cfg.merge_from_dict(args.cfg_options)
  
      # Load output_config from cfg
      output_config = cfg.get('output_config', {})
      if args.out:
          # Overwrite output_config from args.out
          output_config = Config._merge_a_into_b(
              dict(out=args.out), output_config)
  
      # Load eval_config from cfg
      eval_config = cfg.get('eval_config', {})
      if args.eval:
          # Overwrite eval_config from args.eval
          eval_config = Config._merge_a_into_b(
              dict(metrics=args.eval), eval_config)
      if args.eval_options:
          # Add options from args.eval_options
          eval_config = Config._merge_a_into_b(args.eval_options, eval_config)
  
      assert output_config or eval_config, \
          ('Please specify at least one operation (save or eval the '
           'results) with the argument "--out" or "--eval"')
  
      dataset_type = cfg.data.test.type
      if output_config.get('out', None):
          out = output_config['out']
          # make sure the dirname of the output path exists
          mmcv.mkdir_or_exist(osp.dirname(out))
          _, suffix = osp.splitext(out)
          if dataset_type == 'AVADataset':
              assert suffix[1:] == 'csv', ('For AVADataset, the format of the '
                                           'output file should be csv')
          else:
              assert suffix[1:] in file_handlers, (
                  'The format of the output '
                  'file should be json, pickle or yaml')
  
      # set cudnn benchmark
      if cfg.get('cudnn_benchmark', False):
          torch.backends.cudnn.benchmark = True
      cfg.data.test.test_mode = True
  
      if cfg.model.get('test_cfg') is None and cfg.get('test_cfg') is None:
          cfg.model.setdefault('test_cfg',
                               dict(average_clips=args.average_clips))
      else:
          # You can set average_clips during testing, it will override the
          # original settting
          if args.average_clips is not None:
              if cfg.model.get('test_cfg') is not None:
                  cfg.model.test_cfg.average_clips = args.average_clips
              else:
                  cfg.test_cfg.average_clips = args.average_clips
  
      # init distributed env first, since logger depends on the dist info.
      if args.launcher == 'none':
          distributed = False
      else:
          distributed = True
          init_dist(args.launcher, **cfg.dist_params)
  
      # The flag is used to register module's hooks
      cfg.setdefault('module_hooks', [])
  
      # build the dataloader
      dataset = build_dataset(cfg.data.test, dict(test_mode=True))
      dataloader_setting = dict(
          videos_per_gpu=cfg.data.get('videos_per_gpu', 1),
          workers_per_gpu=cfg.data.get('workers_per_gpu', 1),
          dist=distributed,
          shuffle=False)
      dataloader_setting = dict(dataloader_setting,
                                **cfg.data.get('test_dataloader', {}))
      data_loader = build_dataloader(dataset, **dataloader_setting)
  
      # build the model and load checkpoint
      model = build_model(
          cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
  
      register_module_hooks(model.backbone, cfg.module_hooks)
  
      fp16_cfg = cfg.get('fp16', None)
      if fp16_cfg is not None:
          wrap_fp16_model(model)
      load_checkpoint(model, args.checkpoint, map_location='cpu')
  
      if args.fuse_conv_bn:
          model = fuse_conv_bn(model)
  
      if not distributed:
          model = MMDataParallel(model, device_ids=[0])
          outputs = single_gpu_test(model, data_loader)
      else:
          model = MMDistributedDataParallel(
              model.cuda(),
              device_ids=[torch.cuda.current_device()],
              broadcast_buffers=False)
          outputs = multi_gpu_test(model, data_loader, args.tmpdir,
                                   args.gpu_collect)
  
      rank, _ = get_dist_info()
      if rank == 0:
          if output_config.get('out', None):
              out = output_config['out']
              print(f'\nwriting results to {out}')
              dataset.dump_results(outputs, **output_config)
          if eval_config:
              eval_res = dataset.evaluate(outputs, **eval_config)
              for name, val in eval_res.items():
                  print(f'{name}: {val:.04f}')
  
  
  if __name__ == '__main__':
      main()
  